Model_Video_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "from imutils.video import VideoStream\n",
    "import imutils\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from os.path import dirname, join,__file__\n",
    "prototxtPath = 'deploy.prototxt.txt'\n",
    "weightsPath= 'res10_300x300_ssd_iter_140000.caffemodel'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'deploy.prototxt.txt'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prototxtPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "faceNet=cv2.dnn.readNet(weightsPath,prototxtPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dnn_Net 0x1919035d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load model\n",
    "model=load_model(r'custom_4000_32_100.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def increase_brightness(img, value=30):\n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    h, s, v = cv2.split(hsv)\n",
    "\n",
    "    lim = 255 - value\n",
    "    v[v > lim] = 255\n",
    "    v[v <= lim] += value\n",
    "\n",
    "    final_hsv = cv2.merge((h, s, v))\n",
    "    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_and_predict_mask(frame,faceNet,model):\n",
    "    #grab the dimensions of the frame and then construct a blob\n",
    "    (h,w)=frame.shape[:2]\n",
    "    blob=cv2.dnn.blobFromImage(frame,1.0,(300,300),(104.0,177.0,123.0))\n",
    "    \n",
    "    faceNet.setInput(blob)\n",
    "    detections=faceNet.forward()\n",
    "    \n",
    "    #initialize our list of faces, their corresponding locations and list of predictions\n",
    "    \n",
    "    faces=[]\n",
    "    locs=[]\n",
    "    preds=[]\n",
    "    \n",
    "    for i in range(0,detections.shape[2]):\n",
    "        confidence=detections[0,0,i,2]\n",
    "    \n",
    "    \n",
    "        if confidence>0.5:\n",
    "        #we need the X,Y coordinates\n",
    "            box=detections[0,0,i,3:7]*np.array([w,h,w,h])\n",
    "            (startX,startY,endX,endY)=box.astype('int')\n",
    "        \n",
    "            #ensure the bounding boxes fall within the dimensions of the frame\n",
    "            (startX,startY)=(max(0,startX-30),max(0,startY-30))\n",
    "            (endX,endY)=(min(w-1,endX+30), min(h-1,endY+30))\n",
    "        \n",
    "            #extract the face ROI, convert it from BGR to RGB channel, resize it to 96,96 and preprocess it\n",
    "            face=frame[startY:endY, startX:endX]\n",
    "            face=increase_brightness(face)\n",
    "            face=cv2.cvtColor(face,cv2.COLOR_BGR2RGB)\n",
    "            face=cv2.resize(face,(96,96))\n",
    "            face=img_to_array(face)\n",
    "            face=preprocess_input(face)\n",
    "        \n",
    "            faces.append(face)\n",
    "            locs.append((startX,startY,endX,endY))\n",
    "        \n",
    "    #only make a predictions if atleast one face was detected\n",
    "    if len(faces)>0:\n",
    "        faces=np.array(faces,dtype='float32')\n",
    "        preds=model.predict(faces,batch_size=6)\n",
    "        \n",
    "    return (locs,preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs=VideoStream(src=0).start()\n",
    "\n",
    "while True:\n",
    "    #grab the frame from the threaded video stream and resize it\n",
    "    #to have a maximum width of 400 pixels\n",
    "    frame=vs.read()\n",
    "    frame=imutils.resize(frame,width=800)\n",
    "    #detect faces in the frame and preict if they are waring masks or not\n",
    "    (locs,preds)=detect_and_predict_mask(frame,faceNet,model)\n",
    "    \n",
    "    #loop over the detected face locations and their corrosponding loactions\n",
    "    \n",
    "    for (box,pred) in zip(locs,preds):\n",
    "        (startX,startY,endX,endY)=box\n",
    "        (mask,withoutMask)=pred\n",
    "        \n",
    "        #determine the class label and color we will use to draw the bounding box and text\n",
    "        label='Mask' if mask>0.5 else 'No Mask'\n",
    "        color=(0,255,0) if label=='Mask' else (0,0,255)\n",
    "        label=\"{}: {:.2f}%\".format(label,max(mask,withoutMask)*100)\n",
    "        #display the label and bounding boxes\n",
    "        cv2.putText(frame,label,(startX,startY-10),cv2.FONT_HERSHEY_SIMPLEX,0.45,color,2)\n",
    "        \n",
    "        cv2.rectangle(frame,(startX,startY),(endX,endY),color,2)\n",
    "        \n",
    "    #show the output frame\n",
    "    cv2.imshow(\"Frame\",frame)\n",
    "    key=cv2.waitKey(1) & 0xFF\n",
    "    \n",
    "    if key==ord('q'):\n",
    "        break\n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "vs.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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